The critical challenge of Semi-Supervised Learning (SSL) is how to
effectively leverage the limited labeled data and massive unlabeled data to
improve the model's generalization performance. In this paper, we first revisit
the popular pseudo-labeling methods via a unified sample weighting formulation
and demonstrate the inherent quantity-quality trade-off problem of
pseudo-labeling with thresholding, which may prohibit learning. To this end, we
propose SoftMatch to overcome the trade-off by maintaining both high quantity
and high quality of pseudo-labels during training, effectively exploiting the
unlabeled data. We derive a truncated Gaussian function to weight samples based
on their confidence, which can be viewed as a soft version of the confidence
threshold. We further enhance the utilization of weakly-learned classes by
proposing a uniform alignment approach. In experiments, SoftMatch shows
substantial improvements across a wide variety of benchmarks, including image,
text, and imbalanced classification.

本文介绍了基于样本加权统一公式的伪标签方法及其 inherent quantity-quality trade-off 问题，并提出了利用截断高斯函数对置信度加权以实现软的置信度阈值。加强了弱学习类别的利用，在图像分类、文本分类和非平衡分类等多种基准测试中都取得了显著的改进。